Strong absolute accuracy on mixture properties often masks poor recovery of non-ideal behavior, with large drops under strict molecule splits, making transfer to unseen molecules the central challenge.
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10 Pith papers cite this work, alongside 524 external citations. Polarity classification is still indexing.
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LatentFlow is a visual analytics tool that tracks molecular GNN embedding clusters across layers and training states with a modified Sankey diagram, linking them to chemical substructures.
Augmenting SLM prompts with a GNN expert's prediction, confidence, and an extracted important subgraph improves zero-shot toxicity/mutagenicity accuracy on MUTAG and Tox21, with gains up to 74% relative to SMILES-only prompts.
MMGNN decomposes molecular graphs into multi-color subgraphs by atom-type pairs and applies shared message-passing per subgraph, achieving top macro AUC-ROC of 0.838 on classification and best RMSE on ESOL and FreeSolv among tested models.
Simple ML models using Morgan fingerprints predict LNP transfection efficiency better than the graph-based AGILE model, on a refined and publicly released 1,100-lipid dataset.
SALSA factorizes active learning over synthon choices to screen multi-vector molecular spaces up to about two trillion compounds, recovering more than 94 percent of the top-1K molecules in a one-million-molecule benchmark with a small evaluation budget.
POMMix, a graph-based model with attention and cosine similarity heads, extends the Principal Odor Map to predict human perceptual similarity of odor mixtures, reporting a test correlation of 0.78 on a compiled dataset of 865 mixture pairs.
A hierarchical multi-agent LLM system autonomously plans, executes, and debugs quantum chemistry calculations, achieving over 87% success on six benchmark exercise types and two case studies.
A mole-fraction-weighted pooling of molecular embeddings enables a D-MPNN to predict solvation free energies in binary and ternary solvent mixtures, outperforming COSMOtherm on non-aqueous systems.
citing papers explorer
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A Systematic Evaluation of Molecular Mixture Behavior Prediction
Strong absolute accuracy on mixture properties often masks poor recovery of non-ideal behavior, with large drops under strict molecule splits, making transfer to unseen molecules the central challenge.
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LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks
LatentFlow is a visual analytics tool that tracks molecular GNN embedding clusters across layers and training states with a modified Sankey diagram, linking them to chemical substructures.
-
Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools
Augmenting SLM prompts with a GNN expert's prediction, confidence, and an extracted important subgraph improves zero-shot toxicity/mutagenicity accuracy on MUTAG and Tox21, with gains up to 74% relative to SMILES-only prompts.
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MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction
MMGNN decomposes molecular graphs into multi-color subgraphs by atom-type pairs and applies shared message-passing per subgraph, achieving top macro AUC-ROC of 0.838 on classification and best RMSE on ESOL and FreeSolv among tested models.